CN115618749B - Error compensation method for real-time positioning of large unmanned aerial vehicle - Google Patents
Error compensation method for real-time positioning of large unmanned aerial vehicle Download PDFInfo
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Abstract
The invention provides an error compensation method for real-time positioning of a large unmanned aerial vehicle, which comprises the following steps: s1, acquiring an error data set for real-time positioning of an unmanned aerial vehicle; s2, constructing an error model based on the error data set; s3, applying the error model; and S4, updating the error model. The invention fully considers the working mode and working state of each sensor and the problem of data transmission delay, accurately describes the system error of the real-time positioning of the large unmanned aerial vehicle, and avoids the error problem of the matching of the traditional general model and the unmanned aerial vehicle system by combining the sample acquisition and training of the unmanned aerial vehicle in a machine learning mode, thereby remarkably improving the real-time positioning precision of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an error compensation method for real-time positioning of a large unmanned aerial vehicle.
Background
Real-time positioning of the unmanned aerial vehicle is usually directed by an airborne pos system direct sensor. Correction and correction are needed to be made to the eccentricity component of the on-board GNSS, the eccentricity angle of the inertial navigation and the camera and the azimuth element in the camera in general. Although the above correction can greatly improve the positioning accuracy, the measurement error of inertial navigation, the working state of GNSS \ IMU and the positioning error still restrict the accuracy of direct sensor orientation. These systematic errors are particularly significant when the drone performs positioning tasks for long range, small pitch angles.
Disclosure of Invention
The invention aims to provide an error compensation method for real-time positioning of a large unmanned aerial vehicle, and aims to solve the problem that system errors have great influence when the unmanned aerial vehicle executes positioning tasks of long sight distance and small pitch angle.
The invention provides an error compensation method for real-time positioning of a large unmanned aerial vehicle, which comprises the following steps:
s1, acquiring an error data set for real-time positioning of an unmanned aerial vehicle;
s2, constructing an error model based on the error data set;
s3, applying the error model;
and S4, updating the error model.
Further, step S1 includes the following substeps:
s11, setting a calibration field range, and acquiring a high-resolution image based on a satellite or unmanned aerial vehicle platform in the calibration field range;
s12, planning a flight task of the unmanned aerial vehicle;
s13, enabling the attitude of the photoelectric pod to change periodically in the flight process of the unmanned aerial vehicle;
s14, taking video frames from a video shot by the photoelectric pod camera at fixed intervals, telemetering information of the video frame time and telemetering information 1S before the video frame time, and taking a video frame set I;
s15, for each frame of image in the video frame set I, calculating the geographical position of a pixel in the image according to POS data and executing geographical correction to obtain a new image set J;
s16, respectively carrying out feature matching on the high-resolution images and the images in the new image set J, and endowing the pixels in the video frame set I with accurate geographic coordinates according to the matching result to obtain a point set
Sum point set
；
S17, regarding the ith image in the video frame set I, based on the point set
Sum point set
The provided pixel coordinates and three-dimensional space coordinates adopt a single-image space back intersection method to calculate exterior orientation elements and line elements thereofIs marked as
The angle element is converted into quaternion and then is recorded as
(ii) a Repeating the process, and calculating the exterior orientation element of each image in the video frame set I;
s18, establishing a position error data set and an attitude error data set: for the ith image in the video frame set I, calculating a projection center and a quaternion corrected by an eccentric component and an eccentric angle according to the shooting time and the telemetering information of the previous 1 s:
、
、
and
；
will be provided with
Yaw angle, GNSS operating state and telemetry information solution with respect to the first 1s of a video frame
The yaw angle and the GNSS working state are used as independent variables,
establishing a position error data set as a dependent variable; in the same way, will
And resolving telemetering information of each attitude angle, inertial navigation working state and first 1s of video frame
The attitude angles and the inertial navigation working state are taken as independent variables,
establishing an attitude error data set as a dependent variable;
and repeating the process until all the images in the video frame set I are calculated to obtain an error data set.
Further, in step S12, the unmanned aerial vehicle flies around the center O of the calibration yard in the calibration yard by using a petal-shaped flight path.
Further, in step S13, the periodic variation of the attitude of the photovoltaic pod during the flight of the unmanned aerial vehicle means that the azimuth angle of the photovoltaic pod is changed
And a pitch angle
As a function of time t, expressed as:
in the formula,Tthe period of the attitude change of the photoelectric pod;Tof values not greater than the estimated time of flight of the planned route
；
To representtIs divided byTThe remainder of (1);
and
respectively representing the pitch angle of the photoelectric gondolaMinimum and maximum values of (d);sis a positive integer represented inTIn the period, the pitch angle
The period of time of (a) varies.
Further, step S15 includes:
for the ith image in the video frame set I, directionally calculating an image point set by adopting a direct sensor according to POS data
To obtain a point set
(ii) a Wherein, the image point set
W is the width of the image, h is the height of the image;
set of points
Sum point set
Establishing a quadratic polynomial model expressed as:
wherein x and y are point sets
The coordinates of (a); x and Y are
The coordinates of (a); a is 0 、a 1 、a 2 、a 3 、a 4 、a 5 、b 0 、b 1 、b 2 、b 3 、b 4 、b 5 To be a coefficient of undeterminationThe method is obtained by a least square method; calculating the geographic coordinates of each pixel of the ith image in the video frame set I by using a quadratic polynomial model, and then performing gray level resampling to obtain a new image;
and repeating the steps, and processing all the images in the video frame set I to obtain a new image set J.
Further, step S16 includes:
for the ith image in the new image set J, matching a local area of the high-resolution image according to the geographic position of the ith image; performing feature matching by taking the local high-resolution image and the ith image in the new image set J as data sources, screening out a correct matching relation by using a RANSAC algorithm, and respectively marking feature point sets of the image in the high-resolution image and the new image set J as feature point sets
、
；
Collecting points by formula (3)
、
Converting the pixel coordinates of the midpoint into geographic coordinates, and recording the geographic coordinates as a point set
Sum point set
：
Wherein A is a homogeneous form of affine transformation matrix of the ith image in the high-resolution image or new image set J, and x and y represent
Or
X and Y represent geographical coordinates;
set points
Substituting each point into a quadratic polynomial model to calculate the pixel coordinate of each point in the ith image in the video frame set I, and recording the result as a point set
；
For point sets
Substituting the geographical coordinates of each point into a high-precision DEM to calculate the elevation; will point set
The geographical coordinates and the obtained elevation of each point are combined to form three-dimensional space coordinates, and the result is recorded as a point set
；
And repeating the steps until all the images in the new image set J are processed.
Further, step S2 includes the following sub-steps:
s21, optimizing a BP neural network with initial weight and threshold value based on a genetic algorithm to serve as an error model;
s22, standardizing the error data set, disorganizing the sequence, and dividing the error data set into a training set and a test set according to a certain proportion;
s23, searching the initial weight and the threshold value of the BP neural network by using a genetic algorithm;
s24, initializing the BP neural network according to the initial weight and the threshold value in the step S23, taking the square sum of the output of the BP neural network and the dependent variable as a loss function, and continuously updating the weight value and the threshold value of the BP neural network by inputting a training set to obtain an error model;
s25, determining the prediction precision, robustness and convergence of the error model according to the performance of the error model on the training set and the test set; if the error model is not good enough, go back to step S21 to adjust the hyperparameter of BP neural network.
Further, step S3 includes the following sub-steps:
s31, calculating the projection center and quaternion of the telemetering information at the current time and the previous 1S time after correction of the eccentric component and the eccentric angle:
、
、
and
；
s32, mixing
Yaw angle, GNSS operating state and telemetry resolution with respect to the first 1s of the video frame
Inputting the yaw angle and the GNSS working state into a position error model to obtain a projection center compensation value
(ii) a In the same way, will
Calculation of telemetering information of each attitude angle, inertial navigation working state and first 1s of video frame
Inputting each attitude angle and inertial navigation working state into an attitude error model to obtain a quaternion compensation value
；
S33, calculating the compensated projection center S and quaternion q:
in the formula,
in order to compensate for the center of the projection before compensation,
in order to compensate for the pre-quaternion,
is a quaternion addition method;
and S34, determining a photographic ray equation of any image point according to the projection center S and the quaternion q, wherein the intersection point of the photographic ray equation and the earth surface is the geographic coordinate of the image point.
Further, step S4 includes the following sub-steps:
s41, the calibration task obtains an error data set according to the step S1; judging whether the image principal point coordinates fall in the coverage area of the high-resolution image according to a certain frequency in daily flight, if so, executing steps S14-S18 to obtain an error data set;
and S42, after the number of the new error data sets is accumulated to reach a preset data volume, randomly extracting data with the same data volume as the new error data sets from the original error data sets, combining the extracted data and the new error data sets into a new training set, and updating the original error model in a mode of inputting the new training set into the original error model for training.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the invention solves the problem of control points by utilizing high-resolution images, only needs to arrange a small number of control points in the early stage, and saves the workload. The position of the image point in the rear intersection is obtained by feature matching, so that the whole process is automated.
2. The error model fully considers the working mode and working state of each sensor and the problem of data transmission delay, accurately describes the system error of the real-time positioning of the large unmanned aerial vehicle, and avoids the error problem of the matching of the traditional general model and the unmanned aerial vehicle system by combining the sample acquisition and training of the specific unmanned aerial vehicle through a machine learning mode, thereby remarkably improving the real-time positioning precision of the unmanned aerial vehicle.
3. The positioning technology has great advantages in emergency search and rescue and battlefield reconnaissance, is flexible and changeable to operate, does not limit the postures of the airplane and the load during ground observation, and can realize real-time high-precision positioning.
4. The invention has simple upgrading and maintenance. Besides the calibration task, the aircraft can accumulate data when performing a daily flight task, and autonomously learn in real time to improve the accuracy of the model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of an error compensation method for real-time positioning of a large unmanned aerial vehicle in the embodiment of the present invention.
Fig. 2 is a schematic route view of a petal-shaped flight route for flying when planning a flight mission of an unmanned aerial vehicle according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1, the present embodiment provides an error compensation method for real-time positioning of a large unmanned aerial vehicle, including the following steps:
s1, acquiring an error data set of real-time positioning of the unmanned aerial vehicle:
s11, setting a calibration field range, and acquiring a high-resolution image based on a satellite or unmanned aerial vehicle platform in the calibration field range;
s12, planning the flight mission of the unmanned aerial vehicle: respectively get the sets
And the middle element is used as the flight height H, so that the unmanned aerial vehicle flies around the center O of the calibration field in the calibration field by adopting a petal-shaped flight route.
As shown in fig. 2, the unmanned aerial vehicle starts from the calibration center O, flies to a point a, then turns according to a set turning radius, flies to a point B, and returns to the point O to complete the half 8' flying of the OABO. Then flight in BO direction completes half '8' of OFGO flight. And so on until to
The direction flies back to point O. The complete flight path is OABOFGOCDOOEFOBCOGHODEO.
And S13, enabling the attitude of the photoelectric pod to change periodically in the flight process of the unmanned aerial vehicle.
Specifically, the fact that the attitude of the photoelectric pod is periodically changed in the flight process of the unmanned aerial vehicle means that the azimuth angle of the photoelectric pod is changed
And a pitch angle
As a function of time t, expressed as:
in the formula,Tthe period of the attitude change of the photoelectric pod;Tof values not greater than the estimated time of flight of the planned route
；
To representtIs divided byTThe remainder of (1);
and
respectively representing the minimum value and the maximum value of the pitch angle of the photoelectric pod;sis a positive integer expressed inTIn the period, the pitch angle
The period of time of (a) varies.
And S14, extracting video frames, telemetering information of the video frame time and telemetering information 1S before the video frame time from the video shot by the photoelectric pod camera at fixed intervals, and extracting a video frame set I.
Specifically, the telemetry information should include the position, attitude angle, electro-optic pod attitude angle, focus information, etc. of the aircraft.
And S15, for each frame of image in the video frame set I, calculating the geographical position of a pixel in the image according to POS data and executing geographical correction to obtain a new image set J.
Specifically, for the ith image in the video frame set I, a direct sensor is adopted to directionally calculate an image point set according to POS data
To obtain a point set
. Wherein,
w is the width of the image and h is the height of the image.
Set of points
And
establishing a quadratic polynomial model:
wherein x and y are point sets
With X and Y being
A coordinate of (a) 0 、a 1 、a 2 、a 3 、a 4 、a 5 、b 0 、b 1 、b 2 、b 3 、b 4 、b 5 The undetermined coefficient is obtained by a least square method.
And (3) calculating the geographic coordinate of each pixel of the ith image in the video frame set I according to the formula (2), and performing gray level resampling to obtain a new image.
And repeating the steps, and processing all the images in the video frame set I to obtain a new image set J.
S16, respectively carrying out feature matching on the high-resolution images and the images in the new image set J, and endowing the pixels in the video frame set I with accurate geographic coordinates according to the matching result to obtain a point set
Sum point set
。
Specifically, for the ith image in the new image set J, the local area of the high-resolution image is matched according to the geographic position of the ith image. Performing feature matching by taking the local high-resolution image and the ith image in the new image set J as data sources, screening out a correct matching relation by using a RANSAC (Random Sample Consensus) algorithm, and respectively recording the image feature point sets in the high-resolution image and the new image set J as feature point sets
、
。
The points are collected by the following formula
、
Converting the pixel coordinate of the midpoint into a geographic coordinate, and recording the geographic coordinate as a point set
Sum point set
：
Wherein A is a homogeneous form of affine transformation matrix of the ith image in the high-resolution image or new image set J, and x and y represent
Or
X, Y represent geographical coordinates.
Will point set
Substituting each point into the formula (2) to calculate the pixel coordinate of the ith image of each point in the video frame set I, and recording the result as a point set
。
For point sets
And (4) substituting the geographical coordinates of each point into a high-precision DEM (Digital Elevation Model) to obtain the Elevation. Will point set
The geographical coordinates and the obtained elevation of each point are combined to form three-dimensional space coordinates, and the result is recorded as a point set
。
And repeating the steps until all the images in the new image set J are processed.
S17, for the ith image in the video frame set I, based on the point set
Sum point set
The provided pixel coordinates and three-dimensional space coordinates are calculated by adopting a single-image space back intersection method, and line elements of the exterior orientation coordinates are recorded as
The angle element is converted into quaternion and then is recorded as
. And repeating the process, and calculating the external orientation element of each image in the video frame set I.
And S18, establishing a position error data set and an attitude error data set.
Specifically, for the ith image in the video frame set I, calculating the projection center and quaternion corrected by the eccentric component and the eccentric angle according to the shooting time and the telemetering information of the previous 1 s:
、
、
and
。
will be provided with
Yaw angle, GNSS operating state and telemetry resolution with respect to the first 1s of the video frame
The yaw angle and the GNSS working state are used as independent variables,
establishing a position error data set as a dependent variable;in the same way, will
Calculation of telemetering information of each attitude angle, inertial navigation working state and first 1s of video frame
The attitude angles and the inertial navigation working state are taken as independent variables,
an attitude error data set is established as a dependent variable.
And repeating the process until all the images in the video frame set I are calculated to obtain an error data set.
S2, constructing an error model based on the error data set:
respectively constructing a position error model and an attitude error model by adopting the steps S21-S25 for the position error data set and the attitude error data set:
and S21, using a BP neural network for optimizing initial weight and threshold value based on a genetic algorithm as an error model.
The BP neural network hyper-parameter is set as follows: the number of the hidden layers is one, the initial number of the hidden layers is determined through an empirical formula, and then iterative adjustment is carried out according to results. The activation function is sigmoid, the optimization method is Adam, and the learning rate is 0.01. The number of input layer nodes is determined from the error data set, and the output layer nodes are 3 (position error model) and 4 (attitude error model).
S22, the error data set is standardized and broken, and the error data set is divided into a training set and a testing set according to a certain proportion (such as 7.
And S23, searching the initial weight and the threshold value of the BP neural network by using a genetic algorithm.
And S24, initializing the BP neural network according to the initial weight and the threshold value in the step S23, taking the square sum of the output of the BP neural network and the dependent variable as a loss function, and continuously updating the weight value and the threshold value of the BP neural network by inputting a training set to obtain an error model.
And S25, determining the prediction precision, robustness and convergence of the error model according to the performance of the error model on the training set and the test set. If the error model is not good enough, go back to step S21 to adjust the hyperparameter of BP neural network.
S3, applying the error model:
s31, calculating the projection center and quaternion of the telemetering information at the current time and the previous 1S time after correction of the eccentric component and the eccentric angle:
、
、
and
。
s32, mixing
Yaw angle, GNSS operating state and telemetry information solution with respect to the first 1s of a video frame
Inputting the yaw angle and the GNSS working state into a position error model to obtain a projection center compensation value
(ii) a In the same way, will
Calculation of telemetering information of each attitude angle, inertial navigation working state and first 1s of video frame
Inputting each attitude angle and inertial navigation working state into an attitude error model to obtain a quaternion compensation value
。
S33, calculating the compensated projection center S and quaternion q:
in the formula,
in order to compensate for the center of the projection before compensation,
in order to compensate for the pre-quaternion,
is quaternion addition.
And S34, determining a photographing ray equation of any image point according to the projection center S and the quaternion q, wherein the intersection point of the photographing ray equation and the earth surface is the geographic coordinate of the image point.
S4, updating the error model:
s41, the calibration task obtains an error data set according to the step S1; judging whether the image principal point coordinates fall in the coverage area of the high-resolution image according to a certain frequency in daily flight, if so, executing steps S14-S18 to obtain an error data set;
and S42, after the number of the new error data sets is accumulated to reach a preset data volume (such as 1000 error data sets), randomly extracting data with the same data volume as the new error data sets from the original error data sets, combining the extracted data and the new error data sets into a new training set, and updating the original error model in a mode of inputting the new training set into the original error model for training. The model training method is the same as step S21, and the learning rate is adjusted to 0.001.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The error compensation method for real-time positioning of the large unmanned aerial vehicle is characterized by comprising the following steps of:
s1, acquiring an error data set for real-time positioning of an unmanned aerial vehicle;
s2, constructing an error model based on the error data set;
s3, applying the error model;
s4, updating the error model;
step S1 includes the following substeps:
s11, setting a calibration field range, and acquiring a high-resolution image based on a satellite or unmanned aerial vehicle platform in the calibration field range;
s12, planning a flight task of the unmanned aerial vehicle;
s13, enabling the attitude of the photoelectric pod to change periodically in the flight process of the unmanned aerial vehicle;
s14, taking video frames from a video shot by the photoelectric pod camera at fixed intervals, telemetering information of the video frame time and telemetering information 1S before the video frame time, and taking a video frame set I;
s15, for each frame of image in the video frame set I, calculating the geographical position of a pixel in the image according to POS data and executing geographical correction to obtain a new image set J;
s16, respectively carrying out feature matching on the high-resolution images and the images in the new image set J, and endowing the pixels in the video frame set I with accurate geographic coordinates according to the matching result to obtain a point set
And point set>
；
S17, regarding the ith image in the video frame set I, based on the point set
And point set>
The provided pixel coordinate and three-dimensional space coordinate are calculated by adopting a single-image space back intersection method, and the line element is recorded as ^ er>
The angle element is converted into quaternion and then recorded as->
(ii) a Repeating the process, and calculating the exterior orientation element of each image in the video frame set I;
s18, establishing a position error data set and an attitude error data set: for the ith image in the video frame set I, calculating a projection center and a quaternion corrected by an eccentric component and an eccentric angle according to the shooting time and the telemetering information of the previous 1 s:
、
、/>
and &>
；
Will be provided with
Based on the measured values of the global navigation system, yaw angle, GNSS operating state and/or telemetry information resolved with respect to the first 1s of the video frame>
Based on the deviation angle and the GNSS operating state as arguments>
Establishing a position error data set as a dependent variable; in the same way, will->
And each attitude angle, inertial navigation working state and telemetering information resolving of 1s before video frame>
Each attitude angle and inertial navigation working state are used as independent variables,
establishing an attitude error data set as a dependent variable;
repeating the process until all the images in the video frame set I are calculated to obtain an error data set;
step S15 includes:
for the ith image in the video frame set I, directionally calculating an image point set by adopting a direct sensor according to POS data
Get the point set->
(ii) a Wherein, the image point set>
W is the width of the image, h is the height of the image;
set of points
And point set>
Establishing a quadratic polynomial model expressed as:
wherein x and y are point sets
The coordinates of (a); x and Y are->
The coordinates of (a); a is 0 、a 1 、a 2 、a 3 、a 4 、a 5 、b 0 、b 1 、b 2 、b 3 、b 4 、b 5 The undetermined coefficient is obtained by a least square method; calculating the geographic coordinates of each pixel of the ith image in the video frame set I by using a quadratic polynomial model, and then performing gray level resampling to obtain a new image;
repeating the steps, and processing all the images in the video frame set I to obtain a new image set J;
step S16 includes:
for the ith image in the new image set J, matching a local area of the high-resolution image according to the geographic position of the ith image; performing feature matching by taking the local high-resolution image and the ith image in the new image set J as data sources, screening out a correct matching relation by using a RANSAC algorithm, and respectively marking feature point sets of the image in the high-resolution image and the new image set J as feature point sets
、
；
By the formula (3) to collect points
、/>
The pixel coordinate of the midpoint is converted into a geographic coordinate, and the geographic coordinate is recorded as a point set>
Sum point set>
：
Wherein A is a homogeneous form of affine transformation matrix of the ith image in the high-resolution image or new image set J, and x and y represent
Or>
X and Y represent geographical coordinates;
set points
Substituting each point into a quadratic polynomial model to calculate the pixel coordinate of each point in the ith image in the video frame set I, and recording the result as a point set ^ and ^>
；
For point sets
Substituting the geographical coordinates of each point into a high-precision DEM to obtain the elevation; collect the point>
The geographic coordinate of each point and the obtained elevation are combined to form a three-dimensional space coordinate, and the result is recorded as a point set->
；
And repeating the steps until all the images in the new image set J are processed.
2. The method of claim 1, wherein in step S12, the UAV flies around the center O of the calibration center in the calibration field by a petal-shaped flight path.
3. The error compensation method for the real-time positioning of the large unmanned aerial vehicle as claimed in claim 2, wherein in step S13, the step of periodically changing the attitude of the photovoltaic pod during the flight of the unmanned aerial vehicle means that the azimuth angle of the photovoltaic pod is changed
And pitch angle->
As a function of time t, expressed as:
in the formula,Tthe period of the attitude change of the photoelectric pod;Tof values not greater than the estimated time of flight of the planned route
；
To representtIs divided byTThe remainder of (1); />
And &>
Respectively representing the minimum value and the maximum value of the pitch angle of the photoelectric pod;sis a positive integer expressed inTIn a cycle, the pitch angle is &>
The period of time of (a) varies. />
4. The error compensation method for the real-time positioning of the large unmanned aerial vehicle according to claim 3, wherein the step S3 comprises the following substeps:
s31, calculating the projection center and quaternion of the telemetering information at the current time and the previous 1S time after correction of the eccentric component and the eccentric angle:
、/>
、/>
and &>
；
S32, mixing
Based on the measured values of the global navigation system, yaw angle, GNSS operating state and/or telemetry information resolved with respect to the first 1s of the video frame>
The yaw angle and the GNSS working state are input into the position error model to obtain a projection center compensation value->
(ii) a In the same way, will->
And each attitude angle, inertial navigation working state and telemetering information resolving of 1s before video frame>
Inputting each attitude angle and inertial navigation working state into an attitude error model to obtain a quaternion compensation value->
；
S33, calculating the compensated projection center S and quaternion q:
in the formula,
in order to centre of projection before compensation>
Is a quaternion before compensation>
Adding quaternion;
and S34, determining a photographing ray equation of any image point according to the projection center S and the quaternion q, wherein the intersection point of the photographing ray equation and the earth surface is the geographic coordinate of the image point.
5. The error compensation method for the real-time positioning of the large unmanned aerial vehicle according to claim 4, wherein the step S4 comprises the following substeps:
s41, the calibration task obtains an error data set according to the step S1; judging whether the image principal point coordinates fall in the coverage area of the high-resolution image according to a certain frequency in daily flight, if so, executing steps S14-S18 to obtain an error data set;
and S42, after the number of the new error data sets is accumulated to reach a preset data volume, randomly extracting data with the same data volume as the new error data sets from the original error data sets, combining the extracted data and the new error data sets into a new training set, and updating the original error model in a mode of inputting the new training set into the original error model for training.
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